Multistate modelling to estimate the excess length of stay associated with meticillin-resistant Staphylococcus aureus colonisation and infection in surgical patients

Angelis, Giulia De; Allignol, Arthur; Murthy, Ananthram; Wolkewitz, Martin; Beyersmann, Jan; Safran, Edith; Schrenzel, Jacques; Pittet, Didier; Harbarth, Stéphan Juergen

doi:10.1016/j.jhin.2011.02.003

Cited by 51 publications

(33 citation statements)

References 23 publications

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“…Patients with ESBL-negative BSI were administratively censored from the date of their infection when assessing LOS due to ESBL-positive BSI. 15 Likewise, patients with ESBL-positive BSI were censored at infection onset when assessing LOS due to ESBL-negative BSI. Nonparametric estimation of transition probabilities was performed using the Aalen-Johansen estimator to provide the matrix of transition probabilities, as described elsewhere.…”

Section: Excess Los Estimationmentioning

confidence: 99%

“…Nonparametric estimation of transition probabilities was performed using the Aalen-Johansen estimator to provide the matrix of transition probabilities, as described elsewhere. 11,15,22 The mean difference in LOS (in days) was computed for each day as the difference between the predicted LOS given the intermediate state ("onset of BSI") being reached or not reached on that day. The overall change in LOS was then computed as an average of these quantities, weighted by the time to BSI among infected patients.…”

Section: Excess Los Estimationmentioning

confidence: 99%

“…Standard errors and confidence intervals (CIs) were derived by 500 bootstrap resampling runs. 15 To compare the instantaneous risk of the 2 BSI cohorts reaching the end point (discharge from acute care or death), the end-of-LOS hazards over the course of time were calculated. End-of-LOS hazard was defined as the number of discharged patients (dead or alive) on a given day divided by the number of patients still hospitalized at the beginning of that day.…”

Section: Excess Los Estimationmentioning

confidence: 99%

“…12 Failing to explicitly address the timing and onset of infection will always result in an overestimation of the attributable hospital LOS and therefore of cost. 10,13,14 Multistate models account for competing outcomes and time-dependent bias 11,15 and have not been used to evaluate the burden of infection by ESBL-positive organisms outside the intensive care context. 16 The primary aim of this study was to quantify the economic figure 1.…”

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confidence: 99%

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Burden of Bloodstream Infection Caused by Extended-Spectrum β-Lactamase–Producing Enterobacteriaceae Determined Using Multistate Modeling at a Swiss University Hospital and a Nationwide Predictive Model

Stewardson

Fankhauser

Angelis

et al. 2013

Infect. Control Hosp. Epidemiol.

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. results. Thirty ESBL-positive and 96 ESBL-negative BSI cases were included. The excess LOS attributable to ESBL-positive and ESBLnegative BSI was 9.4 (95% confidence interval [CI], 0.4-18.4) and 2.6 (95% CI, 0.7-5.9) days, respectively. ESBL positivity was therefore associated with 6.8 excess days and CHF 9,473 per BSI. The adjusted end-of-LOS HRs for ESBL-positive and ESBL-negative BSI were 0.62 (95% CI, 0.43-0.89) and 0.90 (95% CI, 0.74-1.10), respectively. After reimbursement, the average financial loss per acute care episode in ESBL-positive BSI, ESBL-negative BSI, and control cohorts was CHF 48,674, 48,131, and 13,532, respectively. Our predictive model estimated that the nationwide cost of third-generation cephalosporin resistance would increase from CHF 2,084,000 in 2010 to CHF 3,526,000 in 2015.conclusions. This is the first hospital-wide analysis of excess LOS attributable to ESBL positivity determined using multistate modeling to avoid time-dependent bias. These results may inform health-economic evaluations of interventions targeting ESBL control.

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Section: Excess Los Estimationmentioning

confidence: 99%

Section: Excess Los Estimationmentioning

confidence: 99%

Section: Excess Los Estimationmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Burden of Bloodstream Infection Caused by Extended-Spectrum β-Lactamase–Producing Enterobacteriaceae Determined Using Multistate Modeling at a Swiss University Hospital and a Nationwide Predictive Model

Stewardson

Fankhauser

Angelis

et al. 2013

Infect. Control Hosp. Epidemiol.

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“…We illustrate this phenomenon with a real-data example from a hospital setting; details are given in De Angelis et al [29]. The aim of the study was to investigate the additional effect of infection in colonized methicillin-resistant Staphylococcus aureus (MRSA) patients [29].…”

Section: Critical Care Medicine: Nosocomial Infectionsmentioning

confidence: 99%

Time-dependent study entries and exposures in cohort studies can easily be sources of different and avoidable types of bias

Wolkewitz

Allignol

Harbarth

et al. 2012

Journal of Clinical Epidemiology

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Estimation of adjusted expected excess length‐of‐stay associated with ventilation‐acquired pneumonia in intensive care: A multistate approach accounting for time‐dependent mechanical ventilation

Bluhmki¹,

Allignol

Ruckly

et al. 2018

Biometrical J

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The expected excess length-of-stay is an established concept to assess the health and economic impact of nosocomial, that is, hospital-acquired infections such as ventilation-acquired pneumonia in intensive care. Estimation must account for the timing of infection as in a multistate perspective, because common retrospective comparisons yield inflated estimates due to time-dependent bias. Since occurrence of ventilation-acquired pneumonia is closely linked to ventilation status, we suggest a multistate model incorporating time-dependent mechanical ventilation as additional states. The appeal is that the expected excess length-of-stay decomposes into extra days spent under ventilation and not under ventilation. This is not only highly relevant from a patient's perspective regarding quality of life, but also from an economic point of view, because ventilation is a major cost driver. The challenge is that estimation involves complex functionals of the matrix of transition probabilities, which in turn are based on the transition hazards. To address heterogeneity between patients, which is a common phenomenon in observational hospital epidemiology, we apply pseudovalue regression to adjust the ventilation-specific quantities for baseline confounding. The performance of our proposal is assessed by simulation and the methods are illustrated on data provided by 12 French intensive care units. Preliminary results indicate that the expected excess length-of-stay associated with ventilation-acquired pneumonia is mainly triggered by extra days spent under mechanical ventilation, and that the excess is most pronounced for intensive care patients with fewer comorbidities at baseline. We also find that such a decomposition is challenging for early times. Example code is provided.

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Multistate modelling to estimate the excess length of stay associated with meticillin-resistant Staphylococcus aureus colonisation and infection in surgical patients

Cited by 51 publications

References 23 publications

Burden of Bloodstream Infection Caused by Extended-Spectrum β-Lactamase–Producing Enterobacteriaceae Determined Using Multistate Modeling at a Swiss University Hospital and a Nationwide Predictive Model

Burden of Bloodstream Infection Caused by Extended-Spectrum β-Lactamase–Producing Enterobacteriaceae Determined Using Multistate Modeling at a Swiss University Hospital and a Nationwide Predictive Model

Time-dependent study entries and exposures in cohort studies can easily be sources of different and avoidable types of bias

Estimation of adjusted expected excess length‐of‐stay associated with ventilation‐acquired pneumonia in intensive care: A multistate approach accounting for time‐dependent mechanical ventilation

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